A Survey on Software Testing Techniques using Genetic Algorithm

The overall aim of the software industry is to ensure delivery of high quality software to the end user. To ensure high quality software, it is required to test software. Testing ensures that software meets user specifications and requirements. However, the field of software testing has a number of underlying issues like effective generation of test cases, prioritisation of test cases etc which need to be tackled. These issues demand on effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of evolutionary algorithms for automatic test generation has been an area of interest for many researchers. Genetic Algorithm (GA) is one such form of evolutionary algorithms. In this research paper, we present a survey of GA approach for addressing the various issues encountered during software testing.Comment: 13 Page

Measurements, Models, Systems and Design

531 s.

Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms - A Review

In the wind energy industry, the power curve represents the relationship between the “wind speed” at the hub height and the corresponding “active power” to be generated. It is the most versatile condition indicator and of vital importance in several key applications, such as wind turbine selection, capacity factor estimation, wind energy assessment and forecasting, and condition monitoring, among others. Ensuring an effective implementation of the aforementioned applications mostly requires a modeling technique that best approximates the normal properties of an optimal wind turbines operation in a particular wind farm. This challenge has drawn the attention of wind farm operators and researchers towards the “state of the art” in wind energy technology. This paper provides an exhaustive and updated review on power curve based applications, the most common anomaly and fault types including their root-causes, along with data preprocessing and correction schemes (i.e., filtering, clustering, isolation, and others), and modeling techniques (i.e., parametric and non-parametric) which cover a wide range of algorithms. More than 100 references, for the most part selected from recently published journal articles, were carefully compiled to properly assess the past, present, and future research directions in this active domain

Coupling Uncertainty Quantifications of the B-Pillar Stamping Process and the Side Crash using Machine Learning

Les proves de xoc a la indústria automotriu són essencials per verificar l'equip de seguretat d'un vehicle i, per tant, són extremadament exigents. Moltes variables, que sorgeixen per les toleràncies de fabricació, causen incertesa en els resultats d'aquestes proves de xoc altament complexes. Atès que una prova de xoc és tan costosa d'emular i les simulacions són massa costoses des del punt de vista computacional per executar simulacions de Monte-Carlo en elles, calen altres alternatives per quantificar aquesta incertesa en les proves de xoc. L'algorisme AQUA utilitza diferents tècniques d'aprenentatge automàtic per quantificar la incertesa d'una prova de xoc amb les simulacions FEM de l'escenari donat i fa una anàlisi de sensibilitat per als paràmetres corresponents. En aquesta tesi s'aborden característiques addicionals a l'algorisme original. A més de l'aplicació a la prova de col·lisió lateral d'un vehicle fictici, l'algorisme també s'utilitza en el procés d'estampació del pilar B del vehicle per obtenir més detalls sobre el mapatge de gruixos d'aquesta part. El resultat de l'anàlisi del procés d'estampació es combina posteriorment amb la simulació de col·lisió lateral del vehicle fictici per incloure una representació més realista del gruix al model de col·lisió lateral.Las pruebas de choque en la industria automotriz son esenciales para verificar el equipo de seguridad de un vehículo y, por lo tanto, son extremadamente exigentes. Muchas variables, que surgen debido a las tolerancias de fabricación, causan incertidumbre en los resultados de estas pruebas de choque altamente complejas. Dado que una prueba de choque es tan costosa de emular y las simulaciones son demasiado costosas desde el punto de vista computacional para ejecutar simulaciones de Monte-Carlo en ellas, se necesitan otras alternativas para cuantificar esta incertidumbre en las pruebas de choque. El algoritmo AQUA utiliza diferentes técnicas de aprendizaje automático para cuantificar la incertidumbre de una prueba de choque con las simulaciones FEM del escenario dado y realiza un análisis de sensibilidad para los parámetros correspondientes. En esta tesis se abordan características adicionales al algoritmo original. Además de la aplicación en la prueba de colisión lateral de un vehículo ficticio, el algoritmo también se utiliza en el proceso de estampado del pilar B del vehículo para obtener más detalles sobre el mapeo de espesores de esta parte. El resultado del análisis del proceso de estampado se combina posteriormente con la simulación de colisión lateral del vehículo ficticio para incluir una representación más realista del grosor en el modelo de colisión lateral.Crash tests in the automotive industry are essential to check a vehicle's safety equipment and are therefore extremely demanding. A lot of variables, that arise due to manufacturing tolerances, cause uncertainty in the results of these highly complex crash tests. With a crash test being so expensive to emulate and simulations being too computationally expensive to run Monte-Carlo simulations on them, other alternatives are needed to quantify this uncertainty in the crash tests. The AQUA algorithm uses different machine learning techniques to quantify the uncertainty of a crash test with the FEM simulations of the given scenario and do a sensitivity analysis for the corresponding parameters. Additional features to the original algorithm are addressed in this thesis. In addition to the application on the side crash test of a dummy vehicle, the algorithm is also used on the stamping process of the B-pillar of the vehicle to obtain more details about the thickness mapping of this part. The outcome of the analysis for the stamping process is subsequently coupled with the side crash simulation of the dummy vehicle to include a more realistic representation of the thickness in the side crash model

Operational Risk Management using a Fuzzy Logic Inference System

Operational Risk (OR) results from endogenous and exogenous risk factors, as diverse and complex to assess as human resources and technology, which may not be properly measured using traditional quantitative approaches. Engineering has faced the same challenges when designing practical solutions to complex multifactor and non-linear systems where human reasoning, expert knowledge or imprecise information are valuable inputs. One of the solutions provided by engineering is a Fuzzy Logic Inference System (FLIS). Despite the goal of the FLIS model for OR is its assessment, it is not an end in itself. The choice of a FLIS results in a convenient and sound use of qualitative and quantitative inputs, capable of effectively articulating risk management's identification, assessment, monitoring and mitigation stages. Different from traditional approaches, the proposed model allows evaluating mitigation efforts ex-ante, thus avoiding concealed OR sources from system complexity build-up and optimizing risk management resources. Furthermore, because the model contrasts effective with expected OR data, it is able to constantly validate its outcome, recognize environment shifts and issue warning signals.Operational Risk, Fuzzy Logic, Risk Management Classification JEL:G32, C63, D80

Ground-based prediction of aircraft climb : point-mass model vs regression methods

Predicting aircraft trajectories with great accuracy is central to most operational concepts ([1], [2]) and automated tools that are expected to improve the air traffic management (ATM) in the near future. On-board flight management systems predict the aircraft trajectory using a point-mass model describing the forces applied to the center of gravity. This model is formulated as a set of differential algebraic equations that must be integrated over a time interval in order to predict the successive aircraft positions in this interval. The point-mass model requires knowledge of the aircraft state (mass, thrust, etc), atmospheric conditions (wind, temperature), and aircraft intent (target speed or climb rate, for example)

Study On The Effect Of Tool Nose Wear On Surface Roughness And Dimensional Deviation Of Workpiece In Finish Turning Using Machine Vision.

Operasi pemesinan merupakan suatu kaedah umum bagi menghasilkan komponen-komponen mekanikal yang dikeluarkan di segenap pelusuk dunia. Permintaan terhadap perkakas mesin dalam setahun dilaporkan mencecah lebih daripada £10 bilion. The aim of this research is to study the direct effect of tool nose wear which is in contact to the surface profile of workpiece directly, on the surface roughness and dimensional deviation of workpiece using a developed machine vision in finish turning operation